My primary motivation was to explore reviews of different types of movies posted by users located in different regions based on the data source from Twitter. So I chose five recent movies, Murder on the Orient Express, Coco, Justice League, Daddy’s Home 2, Wonder, as my research subjects.
The analysis will help the moviegoers and the movie industry improve box-office results and help people easily decide what kind of movies to see basis their interests.
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| Movie Name | Movie Type | Release Date | Number of Original Tweets | Number of Retweets |
|---|---|---|---|---|
| Coco | Animation | 2017-11-22 | 35598 | 98873 |
| Daddy’s Home 2 | Comedy | 2017-11-10 | 4828 | 4075 |
| Justice League | Action | 2017-11-17 | 125830 | 164643 |
| Murder on the Orient Express | Mystery | 2017-11-10 | 13556 | 4919 |
| Wonder | Drama | 2017-11-17 | 7982 | 12302 |
| Word | Sentiment | Count |
|---|---|---|
| murder | negative | 16441 |
| win | positive | 1224 |
| mystery | negative | 481 |
| love | positive | 236 |
| death | negative | 215 |
| amazing | positive | 201 |
| top | positive | 186 |
| enjoyed | positive | 175 |
| pretty | positive | 169 |
| classic | positive | 165 |
| Word | Sentiment | Count |
|---|---|---|
| win | positive | 21168 |
| prize | positive | 14432 |
| frozen | negative | 7821 |
| perfect | positive | 3546 |
| love | positive | 3045 |
| beautiful | positive | 2923 |
| critics | negative | 2742 |
| cry | negative | 2236 |
| amazing | positive | 2101 |
| challenging | negative | 2047 |
| Word | Sentiment | Count |
|---|---|---|
| win | positive | 7072 |
| love | positive | 5804 |
| marvel | positive | 5324 |
| parody | negative | 4971 |
| injustice | negative | 4308 |
| effectively | positive | 3681 |
| bad | negative | 3247 |
| cool | positive | 3038 |
| prize | positive | 2962 |
| respect | positive | 2923 |
| Word | Sentiment | Count |
|---|---|---|
| win | positive | 1518 |
| prize | positive | 1450 |
| funny | negative | 276 |
| bad | negative | 205 |
| hilarious | positive | 173 |
| top | positive | 121 |
| murder | negative | 114 |
| fucking | negative | 61 |
| love | positive | 57 |
| recommend | positive | 56 |
| Word | Sentiment | Count |
|---|---|---|
| win | positive | 496 |
| magic | positive | 380 |
| love | positive | 326 |
| amazing | positive | 313 |
| kindness | positive | 307 |
| loved | positive | 282 |
| beautiful | positive | 265 |
| hug | positive | 251 |
| wonderful | positive | 251 |
| perfect | positive | 249 |
With the use of plethora of features in R, the analysis will provide key insights about those five movies, including timeline of tweets, user platforms of tweets, retweet network, etc. Also, maps were made to locate the tweets. The data was then reviewed graphically to explore the trending words in twitter about those movies and sentiment analysis was done using word cloud, line plots and horizontal bar charts, to tell us that how different customers react to different types of movies.
Various results and analysis showed that most people in USA prefer action movies, then animation movie, like Coco. The comedy Daddy’s Home 2 is least noticed in twitter. Besides, based on the states maps, people in California, Texas, Florida, New York and Virginia tweets more about those movies. But sentiment analysis, and emoji analysis, all together show us positive reactions of customers associated with the movies. win, love, prize, and Perfect - customers associate movie with positive words like these. This can help movie industry in understanding how branding and associating with these words can further help them improving box office.